非线性估计中无味滤波(UKF)与扩展卡尔曼滤波(EKF)之比较研究

Comparison between Unscented Kalman Filter and Extended Kalman Filter method in Nonlinear Estimation

作为传统非线性估计方法的代表,扩展卡尔曼滤波(EKF)存在明显的缺陷。对于强非线性系统,无味滤波(UKF)由于选用有限个采样点获取系统的近似分布,并无需计算量测方程的Jacobian矩阵,显示出对非线性系统估计的优越性。本文选用了一个应用于航天器相对导航中的非线性估计的例子进行仿真,仿真表明UKF的滤波精度要优于EKF。

As a typical nonlinear estimation method , extended kalman filter （EKF）has its defects. Compared with EKF, Unscented kalman filter （UKF） can achieve better filtering result for nonlinear system. In this paper, a simulation example based on spacecraft relative navigation is presented, and the simulation results demonstrate that the precision of UKF is higher than EKF.